Overview

Dataset statistics

Number of variables25
Number of observations400
Missing cells1009
Missing cells (%)10.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory78.2 KiB
Average record size in memory200.3 B

Variable types

Numeric10
Categorical10
Boolean5

Alerts

wc has a high cardinality: 90 distinct valuesHigh cardinality
al is highly overall correlated with sc and 6 other fieldsHigh correlation
su is highly overall correlated with bgr and 1 other fieldsHigh correlation
bgr is highly overall correlated with su and 1 other fieldsHigh correlation
bu is highly overall correlated with sc and 1 other fieldsHigh correlation
sc is highly overall correlated with al and 2 other fieldsHigh correlation
sod is highly overall correlated with al and 1 other fieldsHigh correlation
pot is highly overall correlated with pcv and 1 other fieldsHigh correlation
hemo is highly overall correlated with al and 9 other fieldsHigh correlation
sg is highly overall correlated with classificationHigh correlation
rbc is highly overall correlated with al and 2 other fieldsHigh correlation
pc is highly overall correlated with al and 4 other fieldsHigh correlation
pcc is highly overall correlated with pcHigh correlation
pcv is highly overall correlated with pot and 7 other fieldsHigh correlation
rc is highly overall correlated with pot and 6 other fieldsHigh correlation
htn is highly overall correlated with al and 5 other fieldsHigh correlation
dm is highly overall correlated with su and 6 other fieldsHigh correlation
cad is highly overall correlated with rcHigh correlation
ane is highly overall correlated with hemo and 2 other fieldsHigh correlation
classification is highly overall correlated with al and 7 other fieldsHigh correlation
pcc is highly imbalanced (51.2%)Imbalance
ba is highly imbalanced (69.0%)Imbalance
cad is highly imbalanced (57.9%)Imbalance
age has 9 (2.2%) missing valuesMissing
bp has 12 (3.0%) missing valuesMissing
sg has 47 (11.8%) missing valuesMissing
al has 46 (11.5%) missing valuesMissing
su has 49 (12.2%) missing valuesMissing
rbc has 152 (38.0%) missing valuesMissing
pc has 65 (16.2%) missing valuesMissing
bgr has 44 (11.0%) missing valuesMissing
bu has 19 (4.8%) missing valuesMissing
sc has 17 (4.2%) missing valuesMissing
sod has 87 (21.8%) missing valuesMissing
pot has 88 (22.0%) missing valuesMissing
hemo has 52 (13.0%) missing valuesMissing
pcv has 70 (17.5%) missing valuesMissing
wc has 105 (26.2%) missing valuesMissing
rc has 130 (32.5%) missing valuesMissing
al has 199 (49.8%) zerosZeros
su has 290 (72.5%) zerosZeros

Reproduction

Analysis started2023-03-16 17:26:23.390707
Analysis finished2023-03-16 17:26:52.928839
Duration29.54 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct76
Distinct (%)19.4%
Missing9
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean51.483376
Minimum2
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-03-16T17:26:53.095637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile19
Q142
median55
Q364.5
95-th percentile74.5
Maximum90
Range88
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation17.169714
Coefficient of variation (CV)0.33350016
Kurtosis0.057840495
Mean51.483376
Median Absolute Deviation (MAD)10
Skewness-0.66825947
Sum20130
Variance294.79908
MonotonicityNot monotonic
2023-03-16T17:26:53.340504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 19
 
4.8%
65 17
 
4.2%
48 12
 
3.0%
50 12
 
3.0%
55 12
 
3.0%
47 11
 
2.8%
56 10
 
2.5%
59 10
 
2.5%
45 10
 
2.5%
54 10
 
2.5%
Other values (66) 268
67.0%
ValueCountFrequency (%)
2 1
 
0.2%
3 1
 
0.2%
4 1
 
0.2%
5 2
0.5%
6 1
 
0.2%
7 1
 
0.2%
8 3
0.8%
11 1
 
0.2%
12 2
0.5%
14 1
 
0.2%
ValueCountFrequency (%)
90 1
 
0.2%
83 1
 
0.2%
82 1
 
0.2%
81 1
 
0.2%
80 4
1.0%
79 1
 
0.2%
78 1
 
0.2%
76 5
1.2%
75 5
1.2%
74 3
0.8%

bp
Real number (ℝ)

Distinct10
Distinct (%)2.6%
Missing12
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean76.469072
Minimum50
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-03-16T17:26:53.545526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile60
Q170
median80
Q380
95-th percentile100
Maximum180
Range130
Interquartile range (IQR)10

Descriptive statistics

Standard deviation13.683637
Coefficient of variation (CV)0.17894342
Kurtosis8.6460952
Mean76.469072
Median Absolute Deviation (MAD)10
Skewness1.605429
Sum29670
Variance187.24194
MonotonicityNot monotonic
2023-03-16T17:26:53.759478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
80 116
29.0%
70 112
28.0%
60 71
17.8%
90 53
13.2%
100 25
 
6.2%
50 5
 
1.2%
110 3
 
0.8%
140 1
 
0.2%
180 1
 
0.2%
120 1
 
0.2%
(Missing) 12
 
3.0%
ValueCountFrequency (%)
50 5
 
1.2%
60 71
17.8%
70 112
28.0%
80 116
29.0%
90 53
13.2%
100 25
 
6.2%
110 3
 
0.8%
120 1
 
0.2%
140 1
 
0.2%
180 1
 
0.2%
ValueCountFrequency (%)
180 1
 
0.2%
140 1
 
0.2%
120 1
 
0.2%
110 3
 
0.8%
100 25
 
6.2%
90 53
13.2%
80 116
29.0%
70 112
28.0%
60 71
17.8%
50 5
 
1.2%

sg
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)1.4%
Missing47
Missing (%)11.8%
Memory size3.2 KiB
1.02
106 
1.01
84 
1.025
81 
1.015
75 
1.005
 
7

Length

Max length5
Median length4
Mean length4.4617564
Min length4

Characters and Unicode

Total characters1575
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.02
2nd row1.02
3rd row1.01
4th row1.005
5th row1.01

Common Values

ValueCountFrequency (%)
1.02 106
26.5%
1.01 84
21.0%
1.025 81
20.2%
1.015 75
18.8%
1.005 7
 
1.8%
(Missing) 47
11.8%

Length

2023-03-16T17:26:53.989655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-16T17:26:54.233309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.02 106
30.0%
1.01 84
23.8%
1.025 81
22.9%
1.015 75
21.2%
1.005 7
 
2.0%

Most occurring characters

ValueCountFrequency (%)
1 512
32.5%
0 360
22.9%
. 353
22.4%
2 187
 
11.9%
5 163
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1222
77.6%
Other Punctuation 353
 
22.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 512
41.9%
0 360
29.5%
2 187
 
15.3%
5 163
 
13.3%
Other Punctuation
ValueCountFrequency (%)
. 353
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1575
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 512
32.5%
0 360
22.9%
. 353
22.4%
2 187
 
11.9%
5 163
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1575
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 512
32.5%
0 360
22.9%
. 353
22.4%
2 187
 
11.9%
5 163
 
10.3%

al
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct6
Distinct (%)1.7%
Missing46
Missing (%)11.5%
Infinite0
Infinite (%)0.0%
Mean1.0169492
Minimum0
Maximum5
Zeros199
Zeros (%)49.8%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-03-16T17:26:54.394203image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3526789
Coefficient of variation (CV)1.3301343
Kurtosis-0.3833766
Mean1.0169492
Median Absolute Deviation (MAD)0
Skewness0.99815724
Sum360
Variance1.8297402
MonotonicityNot monotonic
2023-03-16T17:26:54.590888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 199
49.8%
1 44
 
11.0%
2 43
 
10.8%
3 43
 
10.8%
4 24
 
6.0%
5 1
 
0.2%
(Missing) 46
 
11.5%
ValueCountFrequency (%)
0 199
49.8%
1 44
 
11.0%
2 43
 
10.8%
3 43
 
10.8%
4 24
 
6.0%
5 1
 
0.2%
ValueCountFrequency (%)
5 1
 
0.2%
4 24
 
6.0%
3 43
 
10.8%
2 43
 
10.8%
1 44
 
11.0%
0 199
49.8%

su
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct6
Distinct (%)1.7%
Missing49
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean0.45014245
Minimum0
Maximum5
Zeros290
Zeros (%)72.5%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-03-16T17:26:54.768028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0991913
Coefficient of variation (CV)2.4418742
Kurtosis5.055348
Mean0.45014245
Median Absolute Deviation (MAD)0
Skewness2.4642618
Sum158
Variance1.2082214
MonotonicityNot monotonic
2023-03-16T17:26:54.964575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 290
72.5%
2 18
 
4.5%
3 14
 
3.5%
4 13
 
3.2%
1 13
 
3.2%
5 3
 
0.8%
(Missing) 49
 
12.2%
ValueCountFrequency (%)
0 290
72.5%
1 13
 
3.2%
2 18
 
4.5%
3 14
 
3.5%
4 13
 
3.2%
5 3
 
0.8%
ValueCountFrequency (%)
5 3
 
0.8%
4 13
 
3.2%
3 14
 
3.5%
2 18
 
4.5%
1 13
 
3.2%
0 290
72.5%

rbc
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.8%
Missing152
Missing (%)38.0%
Memory size3.2 KiB
normal
201 
abnormal
47 

Length

Max length8
Median length6
Mean length6.3790323
Min length6

Characters and Unicode

Total characters1582
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rownormal
3rd rownormal
4th rownormal
5th rownormal

Common Values

ValueCountFrequency (%)
normal 201
50.2%
abnormal 47
 
11.8%
(Missing) 152
38.0%

Length

2023-03-16T17:26:55.171493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-16T17:26:55.389410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
normal 201
81.0%
abnormal 47
 
19.0%

Most occurring characters

ValueCountFrequency (%)
a 295
18.6%
n 248
15.7%
o 248
15.7%
r 248
15.7%
m 248
15.7%
l 248
15.7%
b 47
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1582
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 295
18.6%
n 248
15.7%
o 248
15.7%
r 248
15.7%
m 248
15.7%
l 248
15.7%
b 47
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1582
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 295
18.6%
n 248
15.7%
o 248
15.7%
r 248
15.7%
m 248
15.7%
l 248
15.7%
b 47
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1582
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 295
18.6%
n 248
15.7%
o 248
15.7%
r 248
15.7%
m 248
15.7%
l 248
15.7%
b 47
 
3.0%

pc
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.6%
Missing65
Missing (%)16.2%
Memory size3.2 KiB
normal
259 
abnormal
76 

Length

Max length8
Median length6
Mean length6.4537313
Min length6

Characters and Unicode

Total characters2162
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rownormal
3rd rownormal
4th rowabnormal
5th rownormal

Common Values

ValueCountFrequency (%)
normal 259
64.8%
abnormal 76
 
19.0%
(Missing) 65
 
16.2%

Length

2023-03-16T17:26:55.973148image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-16T17:26:56.345920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
normal 259
77.3%
abnormal 76
 
22.7%

Most occurring characters

ValueCountFrequency (%)
a 411
19.0%
n 335
15.5%
o 335
15.5%
r 335
15.5%
m 335
15.5%
l 335
15.5%
b 76
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2162
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 411
19.0%
n 335
15.5%
o 335
15.5%
r 335
15.5%
m 335
15.5%
l 335
15.5%
b 76
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 2162
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 411
19.0%
n 335
15.5%
o 335
15.5%
r 335
15.5%
m 335
15.5%
l 335
15.5%
b 76
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 411
19.0%
n 335
15.5%
o 335
15.5%
r 335
15.5%
m 335
15.5%
l 335
15.5%
b 76
 
3.5%

pcc
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing4
Missing (%)1.0%
Memory size3.2 KiB
notpresent
354 
present
42 

Length

Max length10
Median length10
Mean length9.6818182
Min length7

Characters and Unicode

Total characters3834
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownotpresent
2nd rownotpresent
3rd rownotpresent
4th rowpresent
5th rownotpresent

Common Values

ValueCountFrequency (%)
notpresent 354
88.5%
present 42
 
10.5%
(Missing) 4
 
1.0%

Length

2023-03-16T17:26:56.653367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-16T17:26:57.047518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
notpresent 354
89.4%
present 42
 
10.6%

Most occurring characters

ValueCountFrequency (%)
e 792
20.7%
n 750
19.6%
t 750
19.6%
p 396
10.3%
r 396
10.3%
s 396
10.3%
o 354
9.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3834
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 792
20.7%
n 750
19.6%
t 750
19.6%
p 396
10.3%
r 396
10.3%
s 396
10.3%
o 354
9.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 3834
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 792
20.7%
n 750
19.6%
t 750
19.6%
p 396
10.3%
r 396
10.3%
s 396
10.3%
o 354
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3834
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 792
20.7%
n 750
19.6%
t 750
19.6%
p 396
10.3%
r 396
10.3%
s 396
10.3%
o 354
9.2%

ba
Categorical

Distinct2
Distinct (%)0.5%
Missing4
Missing (%)1.0%
Memory size3.2 KiB
notpresent
374 
present
 
22

Length

Max length10
Median length10
Mean length9.8333333
Min length7

Characters and Unicode

Total characters3894
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownotpresent
2nd rownotpresent
3rd rownotpresent
4th rownotpresent
5th rownotpresent

Common Values

ValueCountFrequency (%)
notpresent 374
93.5%
present 22
 
5.5%
(Missing) 4
 
1.0%

Length

2023-03-16T17:26:57.318048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-16T17:26:57.669846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
notpresent 374
94.4%
present 22
 
5.6%

Most occurring characters

ValueCountFrequency (%)
e 792
20.3%
n 770
19.8%
t 770
19.8%
p 396
10.2%
r 396
10.2%
s 396
10.2%
o 374
9.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3894
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 792
20.3%
n 770
19.8%
t 770
19.8%
p 396
10.2%
r 396
10.2%
s 396
10.2%
o 374
9.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 3894
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 792
20.3%
n 770
19.8%
t 770
19.8%
p 396
10.2%
r 396
10.2%
s 396
10.2%
o 374
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3894
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 792
20.3%
n 770
19.8%
t 770
19.8%
p 396
10.2%
r 396
10.2%
s 396
10.2%
o 374
9.6%

bgr
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct146
Distinct (%)41.0%
Missing44
Missing (%)11.0%
Infinite0
Infinite (%)0.0%
Mean148.03652
Minimum22
Maximum490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-03-16T17:26:57.997353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile78.75
Q199
median121
Q3163
95-th percentile307.25
Maximum490
Range468
Interquartile range (IQR)64

Descriptive statistics

Standard deviation79.281714
Coefficient of variation (CV)0.53555512
Kurtosis4.2255936
Mean148.03652
Median Absolute Deviation (MAD)25
Skewness2.0107732
Sum52701
Variance6285.5902
MonotonicityNot monotonic
2023-03-16T17:26:58.463887image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 10
 
2.5%
93 9
 
2.2%
100 9
 
2.2%
107 8
 
2.0%
131 6
 
1.5%
140 6
 
1.5%
109 6
 
1.5%
92 6
 
1.5%
117 6
 
1.5%
130 6
 
1.5%
Other values (136) 284
71.0%
(Missing) 44
 
11.0%
ValueCountFrequency (%)
22 1
 
0.2%
70 5
1.2%
74 3
0.8%
75 2
 
0.5%
76 4
1.0%
78 3
0.8%
79 3
0.8%
80 2
 
0.5%
81 3
0.8%
82 3
0.8%
ValueCountFrequency (%)
490 2
0.5%
463 1
0.2%
447 1
0.2%
425 1
0.2%
424 2
0.5%
423 1
0.2%
415 1
0.2%
410 1
0.2%
380 1
0.2%
360 2
0.5%

bu
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct118
Distinct (%)31.0%
Missing19
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean57.425722
Minimum1.5
Maximum391
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-03-16T17:26:58.933472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile17
Q127
median42
Q366
95-th percentile162
Maximum391
Range389.5
Interquartile range (IQR)39

Descriptive statistics

Standard deviation50.503006
Coefficient of variation (CV)0.87944921
Kurtosis9.3452886
Mean57.425722
Median Absolute Deviation (MAD)16
Skewness2.6343745
Sum21879.2
Variance2550.5536
MonotonicityNot monotonic
2023-03-16T17:26:59.800752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 15
 
3.8%
25 13
 
3.2%
19 11
 
2.8%
40 10
 
2.5%
15 9
 
2.2%
48 9
 
2.2%
50 9
 
2.2%
18 9
 
2.2%
32 8
 
2.0%
49 8
 
2.0%
Other values (108) 280
70.0%
(Missing) 19
 
4.8%
ValueCountFrequency (%)
1.5 1
 
0.2%
10 2
 
0.5%
15 9
2.2%
16 7
1.8%
17 7
1.8%
18 9
2.2%
19 11
2.8%
20 7
1.8%
21 1
 
0.2%
22 6
1.5%
ValueCountFrequency (%)
391 1
0.2%
322 1
0.2%
309 1
0.2%
241 1
0.2%
235 1
0.2%
223 1
0.2%
219 1
0.2%
217 1
0.2%
215 1
0.2%
208 1
0.2%

sc
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct84
Distinct (%)21.9%
Missing17
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean3.0724543
Minimum0.4
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-03-16T17:27:00.132440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.5
Q10.9
median1.3
Q32.8
95-th percentile11.89
Maximum76
Range75.6
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation5.7411261
Coefficient of variation (CV)1.8685798
Kurtosis79.304345
Mean3.0724543
Median Absolute Deviation (MAD)0.6
Skewness7.5095383
Sum1176.75
Variance32.960529
MonotonicityNot monotonic
2023-03-16T17:27:00.371960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2 40
 
10.0%
1.1 24
 
6.0%
0.5 23
 
5.8%
1 23
 
5.8%
0.9 22
 
5.5%
0.7 22
 
5.5%
0.6 18
 
4.5%
0.8 17
 
4.2%
2.2 10
 
2.5%
1.5 9
 
2.2%
Other values (74) 175
43.8%
(Missing) 17
 
4.2%
ValueCountFrequency (%)
0.4 1
 
0.2%
0.5 23
5.8%
0.6 18
4.5%
0.7 22
5.5%
0.8 17
4.2%
0.9 22
5.5%
1 23
5.8%
1.1 24
6.0%
1.2 40
10.0%
1.3 8
 
2.0%
ValueCountFrequency (%)
76 1
0.2%
48.1 1
0.2%
32 1
0.2%
24 1
0.2%
18.1 1
0.2%
18 1
0.2%
16.9 1
0.2%
16.4 1
0.2%
15.2 1
0.2%
15 1
0.2%

sod
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct34
Distinct (%)10.9%
Missing87
Missing (%)21.8%
Infinite0
Infinite (%)0.0%
Mean137.52875
Minimum4.5
Maximum163
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-03-16T17:27:00.643121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum4.5
5-th percentile125
Q1135
median138
Q3142
95-th percentile150
Maximum163
Range158.5
Interquartile range (IQR)7

Descriptive statistics

Standard deviation10.408752
Coefficient of variation (CV)0.075684188
Kurtosis85.53437
Mean137.52875
Median Absolute Deviation (MAD)3
Skewness-6.9965686
Sum43046.5
Variance108.34212
MonotonicityNot monotonic
2023-03-16T17:27:00.881054image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
135 40
10.0%
140 25
 
6.2%
141 22
 
5.5%
139 21
 
5.2%
142 20
 
5.0%
138 20
 
5.0%
137 19
 
4.8%
150 17
 
4.2%
136 17
 
4.2%
147 13
 
3.2%
Other values (24) 99
24.8%
(Missing) 87
21.8%
ValueCountFrequency (%)
4.5 1
 
0.2%
104 1
 
0.2%
111 1
 
0.2%
113 2
0.5%
114 2
0.5%
115 1
 
0.2%
120 2
0.5%
122 2
0.5%
124 3
0.8%
125 2
0.5%
ValueCountFrequency (%)
163 1
 
0.2%
150 17
4.2%
147 13
3.2%
146 10
 
2.5%
145 11
2.8%
144 9
 
2.2%
143 4
 
1.0%
142 20
5.0%
141 22
5.5%
140 25
6.2%

pot
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct40
Distinct (%)12.8%
Missing88
Missing (%)22.0%
Infinite0
Infinite (%)0.0%
Mean4.6272436
Minimum2.5
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-03-16T17:27:01.155289image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile3.4
Q13.8
median4.4
Q34.9
95-th percentile5.7
Maximum47
Range44.5
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation3.1939042
Coefficient of variation (CV)0.69023904
Kurtosis142.50591
Mean4.6272436
Median Absolute Deviation (MAD)0.5
Skewness11.582956
Sum1443.7
Variance10.201024
MonotonicityNot monotonic
2023-03-16T17:27:01.372654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
3.5 30
 
7.5%
5 30
 
7.5%
4.9 27
 
6.8%
4.7 17
 
4.2%
4.8 16
 
4.0%
4 14
 
3.5%
4.1 14
 
3.5%
4.4 14
 
3.5%
3.9 14
 
3.5%
3.8 14
 
3.5%
Other values (30) 122
30.5%
(Missing) 88
22.0%
ValueCountFrequency (%)
2.5 2
 
0.5%
2.7 1
 
0.2%
2.8 1
 
0.2%
2.9 3
 
0.8%
3 2
 
0.5%
3.2 3
 
0.8%
3.3 3
 
0.8%
3.4 5
 
1.2%
3.5 30
7.5%
3.6 8
 
2.0%
ValueCountFrequency (%)
47 1
 
0.2%
39 1
 
0.2%
7.6 1
 
0.2%
6.6 1
 
0.2%
6.5 2
0.5%
6.4 1
 
0.2%
6.3 3
0.8%
5.9 2
0.5%
5.8 2
0.5%
5.7 4
1.0%

hemo
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct115
Distinct (%)33.0%
Missing52
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean12.526437
Minimum3.1
Maximum17.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-03-16T17:27:01.614044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum3.1
5-th percentile7.9
Q110.3
median12.65
Q315
95-th percentile16.9
Maximum17.8
Range14.7
Interquartile range (IQR)4.7

Descriptive statistics

Standard deviation2.9125866
Coefficient of variation (CV)0.23251517
Kurtosis-0.47139804
Mean12.526437
Median Absolute Deviation (MAD)2.35
Skewness-0.33509468
Sum4359.2
Variance8.4831608
MonotonicityNot monotonic
2023-03-16T17:27:01.871970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 16
 
4.0%
10.9 8
 
2.0%
13.6 7
 
1.8%
13 7
 
1.8%
9.8 7
 
1.8%
11.1 7
 
1.8%
10.3 6
 
1.5%
11.3 6
 
1.5%
13.9 6
 
1.5%
12 6
 
1.5%
Other values (105) 272
68.0%
(Missing) 52
 
13.0%
ValueCountFrequency (%)
3.1 1
0.2%
4.8 1
0.2%
5.5 1
0.2%
5.6 1
0.2%
5.8 1
0.2%
6 2
0.5%
6.1 1
0.2%
6.2 1
0.2%
6.3 1
0.2%
6.6 1
0.2%
ValueCountFrequency (%)
17.8 3
0.8%
17.7 1
 
0.2%
17.6 1
 
0.2%
17.5 1
 
0.2%
17.4 2
0.5%
17.3 1
 
0.2%
17.2 2
0.5%
17.1 2
0.5%
17 4
1.0%
16.9 2
0.5%

pcv
Categorical

HIGH CORRELATION  MISSING 

Distinct43
Distinct (%)13.0%
Missing70
Missing (%)17.5%
Memory size3.2 KiB
52
 
21
41
 
21
48
 
19
44
 
19
40
 
16
Other values (38)
234 

Length

Max length2
Median length2
Mean length1.9939394
Min length1

Characters and Unicode

Total characters658
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)2.7%

Sample

1st row44
2nd row38
3rd row31
4th row32
5th row35

Common Values

ValueCountFrequency (%)
52 21
 
5.2%
41 21
 
5.2%
48 19
 
4.8%
44 19
 
4.8%
40 16
 
4.0%
43 15
 
3.8%
42 13
 
3.2%
45 13
 
3.2%
36 12
 
3.0%
33 12
 
3.0%
Other values (33) 169
42.2%
(Missing) 70
17.5%

Length

2023-03-16T17:27:02.135160image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
52 21
 
6.4%
41 21
 
6.4%
48 19
 
5.8%
44 19
 
5.8%
40 16
 
4.8%
43 15
 
4.5%
42 13
 
3.9%
45 13
 
3.9%
32 12
 
3.6%
50 12
 
3.6%
Other values (33) 169
51.2%

Most occurring characters

ValueCountFrequency (%)
4 175
26.6%
3 129
19.6%
2 96
14.6%
5 71
10.8%
1 41
 
6.2%
0 38
 
5.8%
8 37
 
5.6%
6 28
 
4.3%
9 23
 
3.5%
7 19
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 657
99.8%
Other Punctuation 1
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 175
26.6%
3 129
19.6%
2 96
14.6%
5 71
10.8%
1 41
 
6.2%
0 38
 
5.8%
8 37
 
5.6%
6 28
 
4.3%
9 23
 
3.5%
7 19
 
2.9%
Other Punctuation
ValueCountFrequency (%)
? 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 658
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 175
26.6%
3 129
19.6%
2 96
14.6%
5 71
10.8%
1 41
 
6.2%
0 38
 
5.8%
8 37
 
5.6%
6 28
 
4.3%
9 23
 
3.5%
7 19
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 175
26.6%
3 129
19.6%
2 96
14.6%
5 71
10.8%
1 41
 
6.2%
0 38
 
5.8%
8 37
 
5.6%
6 28
 
4.3%
9 23
 
3.5%
7 19
 
2.9%

wc
Categorical

HIGH CARDINALITY  MISSING 

Distinct90
Distinct (%)30.5%
Missing105
Missing (%)26.2%
Memory size3.2 KiB
9800
 
11
6700
 
10
9600
 
9
7200
 
9
9200
 
9
Other values (85)
247 

Length

Max length5
Median length4
Mean length4.2169492
Min length1

Characters and Unicode

Total characters1244
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)10.8%

Sample

1st row7800
2nd row6000
3rd row7500
4th row6700
5th row7300

Common Values

ValueCountFrequency (%)
9800 11
 
2.8%
6700 10
 
2.5%
9600 9
 
2.2%
7200 9
 
2.2%
9200 9
 
2.2%
6900 8
 
2.0%
5800 8
 
2.0%
11000 8
 
2.0%
7800 7
 
1.8%
7000 7
 
1.8%
Other values (80) 209
52.2%
(Missing) 105
26.2%

Length

2023-03-16T17:27:02.370054image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9800 11
 
3.7%
6700 10
 
3.4%
9600 9
 
3.1%
7200 9
 
3.1%
9200 9
 
3.1%
6900 8
 
2.7%
5800 8
 
2.7%
11000 8
 
2.7%
9100 7
 
2.4%
9400 7
 
2.4%
Other values (80) 209
70.8%

Most occurring characters

ValueCountFrequency (%)
0 645
51.8%
1 99
 
8.0%
9 75
 
6.0%
6 75
 
6.0%
7 75
 
6.0%
8 67
 
5.4%
5 66
 
5.3%
2 55
 
4.4%
4 50
 
4.0%
3 36
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1243
99.9%
Other Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 645
51.9%
1 99
 
8.0%
9 75
 
6.0%
6 75
 
6.0%
7 75
 
6.0%
8 67
 
5.4%
5 66
 
5.3%
2 55
 
4.4%
4 50
 
4.0%
3 36
 
2.9%
Other Punctuation
ValueCountFrequency (%)
? 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1244
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 645
51.8%
1 99
 
8.0%
9 75
 
6.0%
6 75
 
6.0%
7 75
 
6.0%
8 67
 
5.4%
5 66
 
5.3%
2 55
 
4.4%
4 50
 
4.0%
3 36
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1244
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 645
51.8%
1 99
 
8.0%
9 75
 
6.0%
6 75
 
6.0%
7 75
 
6.0%
8 67
 
5.4%
5 66
 
5.3%
2 55
 
4.4%
4 50
 
4.0%
3 36
 
2.9%

rc
Categorical

HIGH CORRELATION  MISSING 

Distinct46
Distinct (%)17.0%
Missing130
Missing (%)32.5%
Memory size3.2 KiB
5.2
 
18
4.5
 
16
4.9
 
14
4.7
 
11
3.9
 
10
Other values (41)
201 

Length

Max length3
Median length3
Mean length2.8148148
Min length1

Characters and Unicode

Total characters760
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)1.5%

Sample

1st row5.2
2nd row3.9
3rd row4.6
4th row4.4
5th row5

Common Values

ValueCountFrequency (%)
5.2 18
 
4.5%
4.5 16
 
4.0%
4.9 14
 
3.5%
4.7 11
 
2.8%
3.9 10
 
2.5%
5 10
 
2.5%
4.8 10
 
2.5%
4.6 9
 
2.2%
3.4 9
 
2.2%
5.9 8
 
2.0%
Other values (36) 155
38.8%
(Missing) 130
32.5%

Length

2023-03-16T17:27:02.887295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5.2 18
 
6.7%
4.5 16
 
5.9%
4.9 14
 
5.2%
4.7 11
 
4.1%
3.9 10
 
3.7%
5 10
 
3.7%
4.8 10
 
3.7%
4.6 9
 
3.3%
3.4 9
 
3.3%
6.1 8
 
3.0%
Other values (36) 155
57.4%

Most occurring characters

ValueCountFrequency (%)
. 245
32.2%
5 115
15.1%
4 115
15.1%
3 75
 
9.9%
6 52
 
6.8%
2 48
 
6.3%
9 34
 
4.5%
8 27
 
3.6%
7 26
 
3.4%
1 22
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 514
67.6%
Other Punctuation 246
32.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 115
22.4%
4 115
22.4%
3 75
14.6%
6 52
10.1%
2 48
9.3%
9 34
 
6.6%
8 27
 
5.3%
7 26
 
5.1%
1 22
 
4.3%
Other Punctuation
ValueCountFrequency (%)
. 245
99.6%
? 1
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 760
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 245
32.2%
5 115
15.1%
4 115
15.1%
3 75
 
9.9%
6 52
 
6.8%
2 48
 
6.3%
9 34
 
4.5%
8 27
 
3.6%
7 26
 
3.4%
1 22
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 245
32.2%
5 115
15.1%
4 115
15.1%
3 75
 
9.9%
6 52
 
6.8%
2 48
 
6.3%
9 34
 
4.5%
8 27
 
3.6%
7 26
 
3.4%
1 22
 
2.9%

htn
Boolean

Distinct2
Distinct (%)0.5%
Missing2
Missing (%)0.5%
Memory size928.0 B
False
251 
True
147 
(Missing)
 
2
ValueCountFrequency (%)
False 251
62.7%
True 147
36.8%
(Missing) 2
 
0.5%
2023-03-16T17:27:03.133287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

dm
Boolean

Distinct2
Distinct (%)0.5%
Missing2
Missing (%)0.5%
Memory size928.0 B
False
261 
True
137 
(Missing)
 
2
ValueCountFrequency (%)
False 261
65.2%
True 137
34.2%
(Missing) 2
 
0.5%
2023-03-16T17:27:03.320157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

cad
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing2
Missing (%)0.5%
Memory size928.0 B
False
364 
True
 
34
(Missing)
 
2
ValueCountFrequency (%)
False 364
91.0%
True 34
 
8.5%
(Missing) 2
 
0.5%
2023-03-16T17:27:03.752915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

appet
Categorical

Distinct2
Distinct (%)0.5%
Missing1
Missing (%)0.2%
Memory size3.2 KiB
good
317 
poor
82 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1596
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgood
2nd rowgood
3rd rowpoor
4th rowpoor
5th rowgood

Common Values

ValueCountFrequency (%)
good 317
79.2%
poor 82
 
20.5%
(Missing) 1
 
0.2%

Length

2023-03-16T17:27:03.905805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-16T17:27:04.114730image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
good 317
79.4%
poor 82
 
20.6%

Most occurring characters

ValueCountFrequency (%)
o 798
50.0%
g 317
 
19.9%
d 317
 
19.9%
p 82
 
5.1%
r 82
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1596
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 798
50.0%
g 317
 
19.9%
d 317
 
19.9%
p 82
 
5.1%
r 82
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1596
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 798
50.0%
g 317
 
19.9%
d 317
 
19.9%
p 82
 
5.1%
r 82
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1596
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 798
50.0%
g 317
 
19.9%
d 317
 
19.9%
p 82
 
5.1%
r 82
 
5.1%

pe
Boolean

Distinct2
Distinct (%)0.5%
Missing1
Missing (%)0.2%
Memory size928.0 B
False
323 
True
76 
(Missing)
 
1
ValueCountFrequency (%)
False 323
80.8%
True 76
 
19.0%
(Missing) 1
 
0.2%
2023-03-16T17:27:04.305354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

ane
Boolean

Distinct2
Distinct (%)0.5%
Missing1
Missing (%)0.2%
Memory size928.0 B
False
339 
True
60 
(Missing)
 
1
ValueCountFrequency (%)
False 339
84.8%
True 60
 
15.0%
(Missing) 1
 
0.2%
2023-03-16T17:27:04.515459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

classification
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
CKD
250 
NOTCKD
150 

Length

Max length6
Median length3
Mean length4.125
Min length3

Characters and Unicode

Total characters1650
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCKD
2nd rowCKD
3rd rowCKD
4th rowCKD
5th rowCKD

Common Values

ValueCountFrequency (%)
CKD 250
62.5%
NOTCKD 150
37.5%

Length

2023-03-16T17:27:04.688428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-16T17:27:04.916414image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
ckd 250
62.5%
notckd 150
37.5%

Most occurring characters

ValueCountFrequency (%)
C 400
24.2%
K 400
24.2%
D 400
24.2%
N 150
 
9.1%
O 150
 
9.1%
T 150
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1650
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 400
24.2%
K 400
24.2%
D 400
24.2%
N 150
 
9.1%
O 150
 
9.1%
T 150
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1650
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 400
24.2%
K 400
24.2%
D 400
24.2%
N 150
 
9.1%
O 150
 
9.1%
T 150
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1650
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 400
24.2%
K 400
24.2%
D 400
24.2%
N 150
 
9.1%
O 150
 
9.1%
T 150
 
9.1%

Interactions

2023-03-16T17:26:49.001680image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:28.147515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:31.008130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:33.204077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:35.133257image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:37.096075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:39.272183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:41.537757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:44.516768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:46.919139image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:49.187527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:28.402791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:31.209640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:33.382657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:35.333843image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:37.288285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:39.491153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:41.795395image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:44.816844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:47.338934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:49.378820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:28.686898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:31.415087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:33.601359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:35.537446image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:37.509658image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:39.697403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:42.092702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:45.141398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:47.527194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:49.575118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:28.908399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:31.633648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:33.788004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:35.728930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:37.709741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:39.886724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:42.387034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:45.440875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:47.714283image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:49.777382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:29.222573image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:31.989810image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:33.968269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:35.914414image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:38.110341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:40.076563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:42.654671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:45.721340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:47.891464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:49.974370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:29.459640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:32.182840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:34.179270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:36.093210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:38.294486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:40.285426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:42.920258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:45.907649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:48.073139image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:50.183795image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:29.810465image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:32.399027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:34.372413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:36.303535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:38.515340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:40.526664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:43.242796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:46.115756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:48.273157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:50.417567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:30.084216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:32.601916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:34.565162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:36.499606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:38.703826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:40.745341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:43.598815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:46.316820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:48.457097image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:50.631295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:30.382840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:32.808570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:34.742667image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:36.700515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:38.891797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:40.985396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:43.903416image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:46.541250image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:48.647393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:50.829116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:30.689267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:32.983553image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:34.916631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:36.882674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:39.076051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:41.204163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:44.171816image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:46.714019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-16T17:26:48.819948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-03-16T17:27:05.090242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
agebpalsubgrbuscsodpothemosgrbcpcpccbapcvwcrchtndmcadappetpeaneclassification
age1.0000.1230.2130.2810.2990.3090.350-0.1340.072-0.2300.0880.0640.1330.1800.0730.0690.1200.0780.3920.3670.2080.1290.1160.1270.349
bp0.1231.0000.1950.2170.1770.1840.305-0.1370.091-0.2760.1700.3900.2900.1570.1270.4160.1510.4340.3570.2840.0000.2540.1600.2860.442
al0.2130.1951.0000.3580.3720.4950.641-0.5340.053-0.6830.2870.5330.5880.4530.4100.3850.2680.3930.5490.4580.3320.3770.4740.3440.726
su0.2810.2170.3581.0000.6020.2230.356-0.2290.055-0.2960.1830.2130.2220.1970.1860.2400.3910.2180.3700.5490.3820.2570.1650.1450.366
bgr0.2990.1770.3720.6021.0000.1950.359-0.2610.072-0.3490.2090.3640.3870.1760.1040.0000.3290.1040.4460.5760.3060.2500.2070.1350.459
bu0.3090.1840.4950.2230.1951.0000.703-0.4140.212-0.5920.1970.3220.4080.2070.2300.4570.2830.4080.4710.3650.2780.2650.3180.4540.381
sc0.3500.3050.6410.3560.3590.7031.000-0.4970.129-0.7260.1390.2090.2410.0000.0000.3830.3730.3970.1800.1790.1430.1410.2720.3750.185
sod-0.134-0.137-0.534-0.229-0.261-0.414-0.4971.0000.0210.5110.2320.2920.3450.2610.1600.2560.2490.3110.3640.3010.2110.2400.2160.3280.396
pot0.0720.0910.0530.0550.0720.2120.1290.0211.000-0.0630.0390.0000.1850.0000.0000.5110.2270.5580.0590.0160.0000.0750.1350.1680.000
hemo-0.230-0.276-0.683-0.296-0.349-0.592-0.7260.511-0.0631.0000.3220.4890.5520.3680.2320.6910.0900.4710.6050.5210.2710.4310.4360.6900.846
sg0.0880.1700.2870.1830.2090.1970.1390.2320.0390.3221.0000.4350.3850.2840.2040.2960.1480.3170.4190.4500.1580.2740.3520.2490.789
rbc0.0640.3900.5330.2130.3640.3220.2090.2920.0000.4890.4351.0000.4100.0690.1480.5390.2780.4010.2890.3210.1610.2620.2820.1630.542
pc0.1330.2900.5880.2220.3870.4080.2410.3450.1850.5520.3850.4101.0000.5010.3110.5750.1180.5390.3720.2890.1940.3030.4030.3150.452
pcc0.1800.1570.4530.1970.1760.2070.0000.2610.0000.3680.2840.0690.5011.0000.2520.3480.3040.3110.1770.1450.1650.1710.0770.1550.250
ba0.0730.1270.4100.1860.1040.2300.0000.1600.0000.2320.2040.1480.3110.2521.0000.2210.3790.2540.0560.0430.1330.1250.1080.0000.167
pcv0.0690.4160.3850.2400.0000.4570.3830.2560.5110.6910.2960.5390.5750.3480.2211.0000.0860.3140.6050.5220.3900.4550.4660.6200.773
wc0.1200.1510.2680.3910.3290.2830.3730.2490.2270.0900.1480.2780.1180.3040.3790.0861.0000.0630.0000.0850.0000.2400.2260.2820.000
rc0.0780.4340.3930.2180.1040.4080.3970.3110.5580.4710.3170.4010.5390.3110.2540.3140.0631.0000.6430.5380.5560.4710.4530.5990.725
htn0.3920.3570.5490.3700.4460.4710.1800.3640.0590.6050.4190.2890.3720.1770.0560.6050.0000.6431.0000.6000.3120.3330.3600.3360.582
dm0.3670.2840.4580.5490.5760.3650.1790.3010.0160.5210.4500.3210.2890.1450.0430.5220.0850.5380.6001.0000.2560.3130.2960.1670.550
cad0.2080.0000.3320.3820.3060.2780.1430.2110.0000.2710.1580.1610.1940.1650.1330.3900.0000.5560.3120.2561.0000.1350.1520.0000.220
appet0.1290.2540.3770.2570.2500.2650.1410.2400.0750.4310.2740.2620.3030.1710.1250.4550.2400.4710.3330.3130.1351.0000.4060.2410.383
pe0.1160.1600.4740.1650.2070.3180.2720.2160.1350.4360.3520.2820.4030.0770.1080.4660.2260.4530.3600.2960.1520.4061.0000.1910.365
ane0.1270.2860.3440.1450.1350.4540.3750.3280.1680.6900.2490.1630.3150.1550.0000.6200.2820.5990.3360.1670.0000.2410.1911.0000.314
classification0.3490.4420.7260.3660.4590.3810.1850.3960.0000.8460.7890.5420.4520.2500.1670.7730.0000.7250.5820.5500.2200.3830.3650.3141.000

Missing values

2023-03-16T17:26:51.200122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-16T17:26:51.814557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-16T17:26:52.486526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

agebpsgalsurbcpcpccbabgrbuscsodpothemopcvwcrchtndmcadappetpeaneclassification
048.080.01.0201.00.0NaNnormalnotpresentnotpresent121.036.01.2NaNNaN15.44478005.2yesyesnogoodnonoCKD
17.050.01.0204.00.0NaNnormalnotpresentnotpresentNaN18.00.8NaNNaN11.3386000NaNnononogoodnonoCKD
262.080.01.0102.03.0normalnormalnotpresentnotpresent423.053.01.8NaNNaN9.6317500NaNnoyesnopoornoyesCKD
348.070.01.0054.00.0normalabnormalpresentnotpresent117.056.03.8111.02.511.23267003.9yesnonopooryesyesCKD
451.080.01.0102.00.0normalnormalnotpresentnotpresent106.026.01.4NaNNaN11.63573004.6nononogoodnonoCKD
560.090.01.0153.00.0NaNNaNnotpresentnotpresent74.025.01.1142.03.212.23978004.4yesyesnogoodyesnoCKD
668.070.01.0100.00.0NaNnormalnotpresentnotpresent100.054.024.0104.04.012.436NaNNaNnononogoodnonoCKD
724.0NaN1.0152.04.0normalabnormalnotpresentnotpresent410.031.01.1NaNNaN12.44469005noyesnogoodyesnoCKD
852.0100.01.0153.00.0normalabnormalpresentnotpresent138.060.01.9NaNNaN10.83396004yesyesnogoodnoyesCKD
953.090.01.0202.00.0abnormalabnormalpresentnotpresent70.0107.07.2114.03.79.529121003.7yesyesnopoornoyesCKD
agebpsgalsurbcpcpccbabgrbuscsodpothemopcvwcrchtndmcadappetpeaneclassification
39052.080.01.0250.00.0normalnormalnotpresentnotpresent99.025.00.8135.03.715.05263005.3nononogoodnonoNOTCKD
39136.080.01.0250.00.0normalnormalnotpresentnotpresent85.016.01.1142.04.115.64458006.3nononogoodnonoNOTCKD
39257.080.01.0200.00.0normalnormalnotpresentnotpresent133.048.01.2147.04.314.84666005.5nononogoodnonoNOTCKD
39343.060.01.0250.00.0normalnormalnotpresentnotpresent117.045.00.7141.04.413.05474005.4nononogoodnonoNOTCKD
39450.080.01.0200.00.0normalnormalnotpresentnotpresent137.046.00.8139.05.014.14595004.6nononogoodnonoNOTCKD
39555.080.01.0200.00.0normalnormalnotpresentnotpresent140.049.00.5150.04.915.74767004.9nononogoodnonoNOTCKD
39642.070.01.0250.00.0normalnormalnotpresentnotpresent75.031.01.2141.03.516.55478006.2nononogoodnonoNOTCKD
39712.080.01.0200.00.0normalnormalnotpresentnotpresent100.026.00.6137.04.415.84966005.4nononogoodnonoNOTCKD
39817.060.01.0250.00.0normalnormalnotpresentnotpresent114.050.01.0135.04.914.25172005.9nononogoodnonoNOTCKD
39958.080.01.0250.00.0normalnormalnotpresentnotpresent131.018.01.1141.03.515.85368006.1nononogoodnonoNOTCKD